Advances in electronics and telemetry have resulted in the miniaturization of medical devices such that medical devices which previously required large stationary equipment can now be worn about the person, who can be monitored or receive treatment while pursuing normal daily tasks.
One area of such advances has been in the treatment of diabetes. An estimated twenty-six million people in the United States, or about 8% of the population, have diabetes. This percentage is expected to increase in the near-term as the population ages. Wearable glucose monitors and insulin pumps have been developed which allow persons under treatment for diabetes to be monitored and receive insulin while carrying on their day-to-day tasks.
Although many insulin pumps are in use in the field, detailed information on the conditions under which the insulin pumps operate is limited. At best, some post-failure data is manufactured by analysis of defective insulin pumps after they are returned. Unfortunately, such data is highly speculative and does not provide the detailed information on the conditions to which the insulin pumps during day-to-day activities, such as walking or running. Lack of detailed day-to-day information limits improvement of the insulin pumps to meet real-world conditions: additional expense results from over-design where actual conditions are less severe than assumed conditions and additional failures result from under-design where actual conditions are more severe than assumed design conditions.
It would be desirable to have an insulin pump data acquisition device and system that would overcome the above disadvantages.